A Few-Shot Approach to Sign Language Recognition: Can Learning One Language Enable Understanding of All?

Ragib Amin Nihal, Nawara Mahmood Broti

Abstract: Sign language is a unique form of communication in which hand or other body part gestures are used to express oneself. A large proportion of the world’s population has speech and hearing impairments and communicates through sign language. Sign language, like verbal language, varies from country to country. Recent researches on automatic recognition focus on specific sign language of a country and require a large dataset. However, a prevalent issue arises when there is plenty of data available for some sign languages, while other sign languages suffer from data scarcity or non-existence of resources. To tackle this issue, our study presents a novel solution by proposing a few-shot learning approach for automatic sign language recognition. This approach involves training the model using data from a single sign language and then leveraging the acquired knowledge to recognize other sign languages, even when limited data is available for those languages. By bridging the gap between limited data availability and accurate recognition of new Sign Languages via employing this few-shot learning technique, our approach contributes to enhancing communication accessibility for the global sign language community. Our experimental results demonstrate promising performance, showcasing the potential of our model in overcoming the challenges of cross-lingual sign language recognition.

External IDs:dblp:conf/acpr/NihalB23